Tensorflow numpy图像重塑[灰度图像]

Question

我正在尝试使用经过训练的神经网络数据在jupyter笔记本中执行Tensorflow“ object_detection_tutorial.py”，但它会引发ValueError。上面提到的文件是Sentdexs tensorflow教程的一部分，用于youtube上的对象检测。

您可以在这里找到它：（https://www.youtube.com/watch?v=srPndLNMMpk&list=PLQVvvaa0QuDcNK5GeCQnxYnSSaar2tpku&index=6）

我的图片尺寸：490x704。因此，这将导致344960阵列。

但它说： ValueError: cannot reshape array of size 344960 into shape (490,704,3)

我究竟做错了什么？

码：

进口货

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

环保设置

# This is needed to display the images.
%matplotlib inline

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

对象检测导入

from utils import label_map_util

from utils import visualization_utils as vis_util

变数

# What model to download.
MODEL_NAME = 'shard_graph'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt')

NUM_CLASSES = 90

将一个（冻结的）Tensorflow模型加载到内存中。

detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')

加载标签图

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

辅助程式码

def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)

检测

# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'frame_{}.png'.format(i)) for i in range(0, 2) ]

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

--

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    # Definite input and output Tensors for detection_graph
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    # Each box represents a part of the image where a particular object was detected.
    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
    # Each score represent how level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')
    for image_path in TEST_IMAGE_PATHS:
      image = Image.open(image_path)
      # the array based representation of the image will be used later in order to prepare the
      # result image with boxes and labels on it.
      image_np = load_image_into_numpy_array(image)
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      # Actual detection.
      (boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      # Visualization of the results of a detection.
      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8)
      plt.figure(figsize=IMAGE_SIZE)
      plt.imshow(image_np)

脚本的最后一部分抛出错误：

----------------------------------------------------------------------
ValueError                           Traceback (most recent call last)
<ipython-input-62-7493eea60222> in <module>()
     14       # the array based representation of the image will be used later in order to prepare the
     15       # result image with boxes and labels on it.
---> 16       image_np = load_image_into_numpy_array(image)
     17       # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
     18       image_np_expanded = np.expand_dims(image_np, axis=0)

<ipython-input-60-af094dcdd84a> in load_image_into_numpy_array(image)
      2   (im_width, im_height) = image.size
      3   return np.array(image.getdata()).reshape(
----> 4       (im_height, im_width, 3)).astype(np.uint8)

ValueError: cannot reshape array of size 344960 into shape (490,704,3)

编辑：

因此，我更改了此函数的最后一行：

def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)

至：

(im_height, im_width)).astype(np.uint8)

并解决了ValueError。但是现在引发了另一个与数组格式有关的ValueError：

----------------------------------------------------------------------
ValueError                           Traceback (most recent call last)
<ipython-input-107-7493eea60222> in <module>()
     20       (boxes, scores, classes, num) = sess.run(
     21           [detection_boxes, detection_scores, detection_classes, num_detections],
---> 22           feed_dict={image_tensor: image_np_expanded})
     23       # Visualization of the results of a detection.
     24       vis_util.visualize_boxes_and_labels_on_image_array(

~/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    898     try:
    899       result = self._run(None, fetches, feed_dict, options_ptr,
--> 900                          run_metadata_ptr)
    901       if run_metadata:
    902         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1109                              'which has shape %r' %
   1110                              (np_val.shape, subfeed_t.name,
-> 1111                               str(subfeed_t.get_shape())))
   1112           if not self.graph.is_feedable(subfeed_t):
   1113             raise ValueError('Tensor %s may not be fed.' % subfeed_t)

ValueError: Cannot feed value of shape (1, 490, 704) for Tensor 'image_tensor:0', which has shape '(?, ?, ?, 3)'

这是否意味着该tensorflow模型不是为灰度图像设计的？有办法使它起作用吗？

解

多亏了Matan Hugi，它现在可以正常工作了。我要做的就是将此函数更改为：

def load_image_into_numpy_array(image):
    # The function supports only grayscale images
    last_axis = -1
    dim_to_repeat = 2
    repeats = 3
    grscale_img_3dims = np.expand_dims(image, last_axis)
    training_image = np.repeat(grscale_img_3dims, repeats, dim_to_repeat).astype('uint8')
    assert len(training_image.shape) == 3
    assert training_image.shape[-1] == 3
    return training_image

Answer 1

Tensorflow预期输入，其格式为NHWC格式，表示：（BATCH，HEIGHT，WIDTH，CHANNELS）。

第1步-添加最后一个尺寸：

last_axis = -1
grscale_img_3dims = np.expand_dims(image, last_axis)

第2步-重复最后一次标注3次：

dim_to_repeat = 2
repeats = 3
np.repeat(grscale_img_3dims, repeats, dim_to_repeat)

因此，您的功能应为：

def load_image_into_numpy_array(image):
    # The function supports only grayscale images
    assert len(image.shape) == 2, "Not a grayscale input image" 
    last_axis = -1
    dim_to_repeat = 2
    repeats = 3
    grscale_img_3dims = np.expand_dims(image, last_axis)
    training_image = np.repeat(grscale_img_3dims, repeats, dim_to_repeat).astype('uint8')
    assert len(training_image.shape) == 3
    assert training_image.shape[-1] == 3
    return training_image